1) Fisher unlearning method. The first group uses the Fisher information [1]} of the remaining data \(\mathbf {D}\) -\(\mathbf {D_i}\)  and inject optimal noise in order to unlearn the target category [2]}, [3]}, [4]}. The unlearning algorithm takes as input the currently trained model, the subset of the training data \(\mathbf {D_i}\in \mathbf {D}\) , and outputs an updated model given by
\(\mathbf {w}^{\prime }=\mathbf {w}-\mathbf {F}^{-1}\mathbf {\Delta }+\mathbf {\sigma }\mathbf {F}^{-\frac{1}{4}}\mathbf {b}\) 
